Biometric authentication that makes and uses an image from biometric information has been used in a wide variety of fields in recent years. For example, fingerprint authentication that uses a fingerprint that is a type of biometric information has been used in large-scale systems with a larger number of registered users, such as access control for buildings and rooms, border control, and national unique identification (ID) to uniquely identify citizens. In addition, fingerprint authentication has also been used in personal-use terminals such as mobile phones and personal computers (hereinafter referred to as a PC).
Meanwhile, for example, in a large-scale biometric authentication system with a larger number of users registered with biometric information, a fingerprint sensor that has a relatively large area from which a large amount of fingerprint information may be collected at one time is used in many cases. On the other hand, in a personal-use terminal such as a mobile phone and a PC, a small-sized, inexpensive, sweep-type fingerprint sensor is used in many cases.
For example, an example has been known that aims at performing collation efficiently by using a feature vector extracted from a skin pattern image and reliability information corresponding to the feature vector. A technique has also been known in which fingerprint images are classified into several patterns by obtaining a frequency image of the fingerprint image. In addition, an example of a collation apparatus has also been known in which an image including a striped pattern is obtained, a frequency spectrum of the obtained image is obtained, and from the frequency spectrum, a frequency component whose amplitude has an absolute value equal to or larger than a prescribed threshold is selected. In this collation apparatus, when the selected frequency component satisfies the quality standard for an image suitable for collation according to prescribed conditions, the image is reconstructed on the basis of the selected frequency component and collation is performed (see Patent documents 1-3).
An example has also been known in which a feature amount of the pattern of the input is broken down into the vector of its elements, an identification matrix obtained by an identification analysis respectively for each feature vector is prepared in advance, and each feature vector is projected on an identification space defined by the identification matrix to compress the dimension. In this example, after compressing the dimension of the feature vector, the obtained feature vectors are combined, and projected again by the identification matrix to calculate the feature vector. This aims at suppressing a decrease in the effective feature amount for identification in compressing the feature dimension. In addition, an example has also been known in which an edge area whose edge amount is larger than a reference value is identified in an image, and a power spectrum that represents the edge area by a frequency area is generated. This example aims at suppressing incorrect determinations in the image quality determination by identifying the hand-shaking direction of the amplitude value in the power spectrum (for example, see Patent documents 4-5).    Patent document 1: Japanese Laid-open Patent Publication No. 10-177650    Patent document 2: Japanese National Publication of International Patent Application No. 2001-511569    Patent document 3: Japanese National Publication of International Patent Application No. 2007-202912    Patent document 4: Japanese Laid-open Patent Publication No. 2004-192603    Patent document 5: Japanese Laid-open Patent Publication No. 2009-237657